An Intelligent Technique for Effective Multi-Disease Prediction

Using the advanced capabilities of the RapidMiner platform, this study offers a thorough investigation of the use of gradient-boosting trees (GBT) in the crucial field of disease prediction. The main goal of our research is to maximize the potential of GBT algorithms to improve the accuracy and dependability of medical diagnostics greatly. The foundation of our research is the use of a carefully selected dataset of multidiseases that we obtained from Kaggle. This dataset offers a wide range of information that we may use to assist with our analysis. This is essentially a summary of our research project that emphasizes the important results and significant significance of GBT algorithms in illness prediction models. Through our methods, we have been able to obtain amazing prediction performance, which highlights the exciting potential of machine learning in the field of healthcare diagnostics. With this investigation, we hope to further the theoretical understanding of disease prediction and open the door to realworld applications that could improve the results for patients and care worldwide. Our research aims to decipher the inherent difficulties of disease prediction through an effective combination of machine-learning techniques and domain knowledge. The GBT model becomes the primary focus in our pursuit of accurate predictions after being painstakingly created and refined through repeated methods. With an astounding accuracy rate of 99.66 %, our model demonstrates its incredible power and effectiveness in deciphering the complex patterns underlying a wide range of medical diseases.